Development and Validation of an Automated, Real-Time Predictive Model for Postpartum Hemorrhage.

OBJECTIVE: To develop and validate a predictive model for postpartum hemorrhage that can be deployed in clinical care using automated, real-time electronic health record (EHR) data and to compare performance of the model with a nationally published risk prediction tool. METHODS: A multivariable logistic regression model was developed from retrospective EHR data from 21,108 patients delivering at a quaternary medical center between January 1, 2018, and April 30, 2022. Deliveries were divided into derivation and validation sets based on an 80/20 split by date of delivery. Postpartum hemorrhage was defined as blood loss of 1,000 mL or more in addition to postpartum transfusion of 1 or more units of packed red blood cells. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC) and was compared with a postpartum hemorrhage risk assessment tool published by the CMQCC (California Maternal Quality Care Collaborative). The model was then programmed into the EHR and again validated with prospectively collected data from 928 patients between November 7, 2023, and January 31, 2024. RESULTS: Postpartum hemorrhage occurred in 235 of 16,862 patients (1.4%) in the derivation cohort. The predictive model included 21 risk factors and demonstrated an AUC of 0.81 (95% CI, 0.79-0.84) and calibration slope of 1.0 (Brier score 0.013). During external temporal validation, the model maintained discrimination (AUC 0.80, 95% CI, 0.72-0.84) and calibration (calibration slope 0.95, Brier score 0.014). This was superior to the CMQCC tool (AUC 0.69 [95% CI, 0.67-0.70], P<.001). The model maintained performance in prospective, automated data collected with the predictive model in real time (AUC 0.82 [95% CI, 0.73-0.91]). CONCLUSION: We created and temporally validated a postpartum hemorrhage prediction model, demonstrated its superior performance over a commonly used risk prediction tool, successfully coded the model into the EHR, and prospectively validated the model using risk factor data collected in real time. Future work should evaluate external generalizability and effects on patient outcomes; to facilitate this work, we have included the model coefficients and examples of EHR integration in the article.

Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation.

Background: Numerous pressure injury prediction models have been developed using electronic health record data, yet hospital-acquired pressure injuries (HAPIs) are increasing, which demonstrates the critical challenge of implementing these models in routine care. Objective: To help bridge the gap between development and implementation, we sought to create a model that was feasible, broadly applicable, dynamic, actionable, and rigorously validated and then compare its performance to usual care (ie, the Braden scale). Methods: We extracted electronic health record data from 197,991 adult hospital admissions with 51 candidate features. For risk prediction and feature selection, we used logistic regression with a least absolute shrinkage and selection operator (LASSO) approach. To compare the model with usual care, we used the area under the receiver operating curve (AUC), Brier score, slope, intercept, and integrated calibration index. The model was validated using a temporally staggered cohort. Results: A total of 5458 HAPIs were identified between January 2018 and July 2022. We determined 22 features were necessary to achieve a parsimonious and highly accurate model. The top 5 features included tracheostomy, edema, central line, first albumin measure, and age. Our model achieved higher discrimination than the Braden scale (AUC 0.897, 95% CI 0.893-0.901 vs AUC 0.798, 95% CI 0.791-0.803). Conclusions: We developed and validated an accurate prediction model for HAPIs that surpassed the standard-of-care risk assessment and fulfilled necessary elements for implementation. Future work includes a pragmatic randomized trial to assess whether our model improves patient outcomes.

Financial impact of integrated specialty pharmacy efforts to avoid oral anticancer medication waste.

BACKGROUND: The growing number of oral anticancer medications represents a significant portion of pharmacy spending and can be costly for patients. Patients taking oral anticancer medications may experience frequent treatment changes following necessary safety and effectiveness monitoring, often resulting in medication waste. Strategies to avoid medication waste could alleviate the financial burden of these costly therapies on the payer and the patient. OBJECTIVE: To evaluate the impact on waste and cost avoidance of reviewing the amount of medication patients have on hand and the presence of upcoming follow-up (ie, provider visit, laboratory testing, or imaging) before requesting a prescription refill renewal for patients taking oral anticancer medications through an integrated health system specialty pharmacy. METHODS: We performed a retrospective review of patients filling oral anticancer medications prescribed by a Vanderbilt University Medical Center provider and dispensed by Vanderbilt Specialty Pharmacy between January 1, 2020, and December 31, 2020. Specialty pharmacists received a system-generated refill renewal request for oral anticancer medications when the final prescription refill was dispensed, prompting the pharmacist to review the patient's medical record for continued therapy appropriateness and to request a new prescription. If the patient had a sufficient supply on hand to last until an upcoming follow-up (ie, provider visit, imaging, or laboratory assessment), the pharmacist postponed the renewal until after the scheduled follow-up. Patients were included in the analysis if the refill renewal request was postponed after review of the amount of medication on hand and the presence of an upcoming follow-up. Medication outcomes (ie, continued, dose changed, held, medication changed to a different oral anticancer medication, or discontinued) resulting from the follow-up were collected. Cost avoidance in US dollars was assigned based on the outcome of follow-up by calculating the price per unit times the number of units that would have been unused or in excess of what was needed if the medication had been dispensed before the scheduled follow-up. The average wholesale price minus 20% (AWP-20%) and wholesale acquisition cost (WAC) were used to report a range of costs avoided over 12 months. RESULTS: The total cost avoidance over 12 months associated with postponing refill renewal requests in a large academic health system with an integrated specialty pharmacy ranged from $549,187.03 using WAC pricing to $751,994.99 using AWP-20% pricing, with a median cost avoidance per fill of $366.04 (WAC) to $1,931.18 (AWP-20%). Refill renewal requests were postponed in 159 instances for 135 unique patients. After follow-up, medications were continued unchanged in only 2% of postponed renewals, 56% of follow-ups resulted in medication discontinuations, 32% in dose changes, 5% in medication changes, and 5% in medication holds. CONCLUSIONS: Integrated health system specialty pharmacist postponement of refill requests after review of the amount of medication on hand and upcoming follow-up proved effective in avoiding waste and unnecessary medication costs in patients treated with oral anticancer medications at a large academic health system.

A randomized controlled trial evaluating the effects of nurse-led triage of 911 calls.

To better connect non-emergent 911 callers to appropriate care, Washington, DC, routed low-acuity callers to nurses. Nurses could provide non-emergent transportation to a health centre, recommend self-care or return callers to the traditional 911 system. Over about one year, 6,053 callers were randomized (1:1) to receive a business-as-usual response (ncontrol = 3,023) or further triage (ntreatment = 3,030). We report on seven of nine outcomes, which were pre-registered ( https://osf.io/xderw ). The proportion of calls resulting in an ambulance dispatch dropped from 97% to 56% (ß = -1.216 (-1.324, -1.108), P < 0.001), and those resulting in an ambulance transport dropped from 73% to 45% (ß = -3.376 (-3.615, -3.137), P < 0.001). Among those callers who were Medicaid beneficiaries, within 24 hours, the proportion of calls resulting in an emergency department visit for issues classified as non-emergent or primary care physician (PCP) treatable dropped from 29.5% to 25.1% (ß = -0.230 (-0.391, -0.069), P < 0.001), and the proportion resulting in the caller visiting a PCP rose from 2.5% to 8.2% (ß = 1.252 (0.889, 1.615), P < 0.001). Over the longer time span of six months, we failed to detect evidence of impacts on emergency department visits, PCP visits or Medicaid expenditures. From a safety perspective, 13 callers randomized to treatment were eventually diagnosed with a time-sensitive illness, all of whom were quickly triaged to an ambulance response. These short-term effects suggest that nurse-led triage of non-emergent calls can safely connect callers to more appropriate, timely care.

Deep Learning for Automated Measurement of Total Cardiac Volume for Heart Transplantation Size Matching.

Total Cardiac Volume (TCV)-based size matching using Computed Tomography (CT) is a novel technique to compare donor and recipient heart size in pediatric heart transplant that may increase overall utilization of available grafts. TCV requires manual segmentation, which limits its widespread use due to time and specialized software and training needed for segmentation. This study aims to determine the accuracy of a Deep Learning (DL) approach using 3-dimensional Convolutional Neural Networks (3D-CNN) to calculate TCV, with the clinical aim of enabling fast and accurate TCV use at all transplant centers. Ground truth TCV was segmented on CT scans of subjects aged 0-30 years, identified retrospectively. Ground truth segmentation masks were used to train and test a custom 3D-CNN model consisting of a DenseNet architecture in combination with residual blocks of ResNet architecture. The model was trained on a cohort of 270 subjects and a validation cohort of 44 subjects (36 normal, 8 heart disease retained for model testing). The average Dice similarity coefficient of the validation cohort was 0.94 ± 0.03 (range 0.84-0.97). The mean absolute percent error of TCV estimation was 5.5%. There is no significant association between model accuracy and subject age, weight, or height. DL-TCV was on average more accurate for normal hearts than those listed for transplant (mean absolute percent error 4.5 ± 3.9 vs. 10.5 ± 8.5, p = 0.08). A deep learning-based 3D-CNN model can provide accurate automatic measurement of TCV from CT images. This initial study is limited as a single-center study, though future multicenter studies may enable generalizable and more accurate TCV measurement by inclusion of more diverse cardiac pathology and increasing the training data.

(F)utility of preoperative pulmonary function testing in pectus excavatum to assess severity.

PURPOSE: The utility of pulmonary function testing (PFT) in pectus excavatum (PE) has been subject to debate. Although some evidence shows improvement from preoperative to postoperative values, the clinical significance is uncertain. A high failure-to-completion rate for operative PFT (48%) was identified in our large institutional cohort. With such a high non-completion rate, we questioned the overall utility of PFT in the preoperative assessment of PE and sought to evaluate if other measures of PE severity or cardiopulmonary function could explain this finding. METHODS: Demographics, clinical findings, and results from cardiac MRI, PFT (spirometry and plethysmography), and cardiopulmonary exercise tests (CPET) were reviewed in 270 patients with PE evaluated preoperatively between 2015 and 2018. Regression modeling was used to measure associations between PFT completion and cardiopulmonary function. RESULTS: There were no differences in demographics, symptoms, connective tissue disorders, or multiple indices of pectus severity and cardiac deformation in PFT completers versus non-completers. While regression analysis revealed higher RVEF, LVEF, and LVEF-Z scores, lower RV-ESV/BSA, LV-ESV/BSA, and LV-ESV/BSA-Z scores, and abnormal breathing reserve in PFT completers vs. non-completers, these findings were not consistent across continuous and binary analyses. CONCLUSIONS: We found that PFT completers were not significantly different from non-completers in most structural and functional measures of pectus deformity and cardiopulmonary function. Inability to complete PFT is not an indicator of pectus severity.

Identifying antinuclear antibody positive individuals at risk for developing systemic autoimmune disease: development and validation of a real-time risk model.

Objective: Positive antinuclear antibodies (ANAs) cause diagnostic dilemmas for clinicians. Currently, no tools exist to help clinicians interpret the significance of a positive ANA in individuals without diagnosed autoimmune diseases. We developed and validated a risk model to predict risk of developing autoimmune disease in positive ANA individuals. Methods: Using a de-identified electronic health record (EHR), we randomly chart reviewed 2,000 positive ANA individuals to determine if a systemic autoimmune disease was diagnosed by a rheumatologist. A priori, we considered demographics, billing codes for autoimmune disease-related symptoms, and laboratory values as variables for the risk model. We performed logistic regression and machine learning models using training and validation samples. Results: We assembled training (n = 1030) and validation (n = 449) sets. Positive ANA individuals who were younger, female, had a higher titer ANA, higher platelet count, disease-specific autoantibodies, and more billing codes related to symptoms of autoimmune diseases were all more likely to develop autoimmune diseases. The most important variables included having a disease-specific autoantibody, number of billing codes for autoimmune disease-related symptoms, and platelet count. In the logistic regression model, AUC was 0.83 (95% CI 0.79-0.86) in the training set and 0.75 (95% CI 0.68-0.81) in the validation set. Conclusion: We developed and validated a risk model that predicts risk for developing systemic autoimmune diseases and can be deployed easily within the EHR. The model can risk stratify positive ANA individuals to ensure high-risk individuals receive urgent rheumatology referrals while reassuring low-risk individuals and reducing unnecessary referrals.

A cryptic plasmid is among the most numerous genetic elements in the human gut.

Plasmids are extrachromosomal genetic elements that often encode fitness-enhancing features. However, many bacteria carry "cryptic" plasmids that do not confer clear beneficial functions. We identified one such cryptic plasmid, pBI143, which is ubiquitous across industrialized gut microbiomes and is 14 times as numerous as crAssphage, currently established as the most abundant extrachromosomal genetic element in the human gut. The majority of mutations in pBI143 accumulate in specific positions across thousands of metagenomes, indicating strong purifying selection. pBI143 is monoclonal in most individuals, likely due to the priority effect of the version first acquired, often from one's mother. pBI143 can transfer between Bacteroidales, and although it does not appear to impact bacterial host fitness in vivo, it can transiently acquire additional genetic content. We identified important practical applications of pBI143, including its use in identifying human fecal contamination and its potential as an alternative approach to track human colonic inflammatory states.

Assessment of the CLOT (children's likelihood of thrombosis) real-time risk prediction model of hospital-associated venous thromboembolism in children with congenital heart disease.

BACKGROUND: Children with congenital heart disease (CHD) are at high risk for hospital-associated venous thromboembolism (HA-VTE). The children's likelihood of thrombosis (CLOT) trial validated a real-time predictive model for HA-VTE using data extracted from the EHR for pediatric inpatients. We tested the hypothesis that addition of CHD specific data would improve model prediction in the CHD population. METHODS: Model performance in CHD patients from 2010 to 2022, was assessed using 3 iterations of the CLOT model: 1) the original CLOT model, 2) the original model refit using only data from the CHD cohort, and 3) the model updated with the addition of cardiopulmonary bypass time, STAT Mortality Category, height, and weight as covariates. The discrimination of the three models was quantified and compared using AUROC. RESULTS: Our CHD cohort included 1457 patient encounters (median 2.0 IQR [0.5-5.2] years-old). HA-VTE was present in 5% of our CHD cohort versus 1% in the general pediatric population. Several features from the original model were associated with thrombosis in the CHD cohort including younger age, thrombosis history, infectious disease consultation, and EHR coding of a central venous line. Lower height and weight were associated with thrombosis. HA-VTE rate was 12% (18/149) amongst those with STAT Category 4-5 operation versus 4% (49/1256) with STAT Category 1-3 operation (P < .001). Longer cardiopulmonary bypass time (124 [92-205] vs. 94 [65-136] minutes, P < .001) was associated with thrombosis. The AUROC for the original (0.80 95% CI [0.75-0.85]), refit (0.85 [0.81-0.89]), and updated (0.86 [0.81-0.90]) models demonstrated excellent discriminatory ability within the CHD cohort. CONCLUSION: The automated approach with EHR data extraction makes the applicability of such models appealing for ease of clinical use. The addition of cardiac specific features improved model discrimination; however, this benefit was marginal compared to refitting the original model to the CHD cohort. This suggests strong predictive generalized models, such as CLOT, can be optimized for cohort subsets without additional data extraction, thus reducing cost of model development and deployment.

A profile of occupational tasks performed by mounted police officers.

BACKGROUND: Within individual policing organisations, there are a wide variety of units and job roles. OBJECTIVE: To profile the occupational tasks performed by Australian mounted police officers, aiming to offer conditioning insight into this unique job role. METHODS: Thirteen fully qualified and operational mounted police officers (n = 11 females), who served in the mounted police unit for ∼3.3 (±2.3) years, participated in this observational cohort study. Participants completed a survey outlining common occupational tasks and were monitored throughout four consecutive shifts. Participants' heart rate (HR), respiratory rate (RR), and skin temperature (ST) data were collected via wearable monitoring (Equivital EQ-02, Hidalgo, UK) and body position, physical activity undertaken, task effort, and load carriage were recorded by researchers. A one-way ANOVA was used to assess mean differences in physiological measures between the three most reported tasks. RESULTS: Survey identified 130 tasks, with 38 listed as most common. The three most reported mounted police tasks were: 'horse riding' (n = 13, 34%), 'mounted patrols' (n = 10, 26%), and 'horse care' (n = 10, 26%). These were also reported as the most physically demanding. HR during 'horse care' and 'horse riding' were significantly higher than when 'mounted patrolling' (27±7bpm, p = 0.001 and 33±8bpm; p = 0.001, respectively). Mean RR was higher when 'horse riding' when compared to 'horse care' (5.3±1.6brpm) and 'mounted patrol' (8.5±1.9brpm). CONCLUSION: Mounted police officers experience unique physiological challenges throughout their routine occupational tasks. This study highlights the significance of understanding occupation-specific tasks and stressors undertaken by mounted police to develop relevant conditioning, rehabilitation, and monitoring procedures.

Novel Techniques in Imaging Congenital Heart Disease: JACC Scientific Statement.

Recent years have witnessed exponential growth in cardiac imaging technologies, allowing better visualization of complex cardiac anatomy and improved assessment of physiology. These advances have become increasingly important as more complex surgical and catheter-based procedures are evolving to address the needs of a growing congenital heart disease population. This state-of-the-art review presents advances in echocardiography, cardiac magnetic resonance, cardiac computed tomography, invasive angiography, 3-dimensional modeling, and digital twin technology. The paper also highlights the integration of artificial intelligence with imaging technology. While some techniques are in their infancy and need further refinement, others have found their way into clinical workflow at well-resourced centers. Studies to evaluate the clinical value and cost-effectiveness of these techniques are needed. For techniques that enhance the value of care for congenital heart disease patients, resources will need to be allocated for education and training to promote widespread implementation.

The Ozaki Procedure: Standardized Protocol Adoption of a Complex Innovative Procedure.

Background: The Ozaki procedure using autologous pericardium is an interesting but complex alternative for aortic valve replacement. We present a standardized approach to minimize the learning curve and confirm reproducibility. Methods: After careful preparation, from May 2015 to February 2021, an Ozaki procedure was performed on 46 patients age 51 ± 14 years. Seven had unicuspid (15%), 29 bicuspid (63%), and 10 tricuspid (22%) aortic valves, and 2 patients had endocarditis. Endpoints were operative learning curves, perioperative outcomes, intermediate-term valve hemodynamics, reintervention, health-related quality of life (MacNew Heart Disease Health-Related Quality of Life questionnaire), and mortality. Results: Cardiopulmonary bypass and aortic clamp times decreased from 145 to 125 â€‹minutes and 120 to 100 â€‹minutes, respectively, over the first 20 cases, reflecting the learning curve. There was no major perioperative morbidity or mortality. Median postoperative stay was 6.9 days. Aortic regurgitation was mild or less in all but 2 patients who developed moderate aortic regurgitation. Mean aortic valve gradient was 7.9 mmHg postoperatively, 9.2 mmHg by 6 months, and constant thereafter. Left ventricular ejection fraction was 58% preoperatively, 60% at 6 months, and remained stable thereafter. One patient developed infective endocarditis 7 months postoperatively, failed medical management, and underwent valve replacement at 14 months. Two-year survival was 96%, with 1 noncardiac death at 16 months. Health-related quality of life in mental, physical, and emotional domains was better than matched norms, global 6.2 vs. 5.0 (p < 0.0001). Conclusions: Using a well-prepared standardized approach, the Ozaki procedure is reproducible with a short learning curve, excellent hemodynamic performance, and good quality of life.

Isolated mitral valve endocarditis: Patient, disease, and surgical factors that influence outcomes.

OBJECTIVE: The objectives of this study were to investigate patient characteristics, valve pathology, bacteriology, and surgical techniques related to outcome of patients who underwent surgery for isolated native (NVE) or prosthetic (PVE) mitral valve endocarditis. METHODS: From January 2002 to January 2020, 447 isolated mitral endocarditis operations were performed, 326 for NVE and 121 for PVE. Multivariable analysis of time-related outcomes used random forest machine learning. RESULTS: Staphylococcus aureus was the most common causative organism. Of 326 patients with NVE, 88 (27%) underwent standard mitral valve repair, 43 (13%) extended repair, and 195 (60%) valve replacement. Compared with NVE with standard repair, patients who underwent all other operations were older, had more comorbidities, worse cardiac function, and more invasive disease. Hospital mortality was 3.8% (n = 17); 0 (0%) after standard valve repair, 3 (7.0%) after extended repair, 8 (4.1%) after NVE replacement, and 6 (5.0%) after PVE re-replacement. Survival at 1, 5, and 10 years was 91%, 75%, and 62% after any repair and 86%, 62%, and 44% after replacement, respectively. The most important risk factor for mortality was renal failure. Risk-adjusted outcomes, including survival, were similar in all groups. Unadjusted extended repair outcomes, particularly early, were similar or worse than replacement in terms of reinfection, reintervention, regurgitation, gradient, and survival. CONCLUSIONS: A patient- and pathology-tailored approach to surgery for isolated mitral valve endocarditis has low mortality and excellent results. Apparent superiority of standard valve repair is related to patient characteristics and pathology. Renal failure is the most powerful risk factor. In case of extensive destruction, extended repair shows no benefit over replacement.

External validation of a predictive model for reintubation after cardiac surgery: A retrospective, observational study.

STUDY OBJECTIVE: Explore validation of a model to predict patients' risk of failing extubation, to help providers make informed, data-driven decisions regarding the optimal timing of extubation. DESIGN: We performed temporal, geographic, and domain validations of a model for the risk of reintubation after cardiac surgery by assessing its performance on data sets from three academic medical centers, with temporal validation using data from the institution where the model was developed. SETTING: Three academic medical centers in the United States. PATIENTS: Adult patients arriving in the cardiac intensive care unit with an endotracheal tube in place after cardiac surgery. INTERVENTIONS: Receiver operating characteristic (ROC) curves and concordance statistics were used as measures of discriminative ability, and calibration curves and Brier scores were used to assess the model's predictive ability. MEASUREMENTS: Temporal validation was performed in 1642 patients with a reintubation rate of 4.8%, with the model demonstrating strong discrimination (optimism-corrected c-statistic 0.77) and low predictive error (Brier score 0.044) but poor model precision and recall (Optimal F1 score 0.29). Combined domain and geographic validation were performed in 2041 patients with a reintubation rate of 1.5%. The model displayed solid discriminative ability (optimism-corrected c-statistic = 0.73) and low predictive error (Brier score = 0.0149) but low precision and recall (Optimal F1 score = 0.13). Geographic validation was performed in 2489 patients with a reintubation rate of 1.6%, with the model displaying good discrimination (optimism-corrected c-statistic = 0.71) and predictive error (Brier score = 0.0152) but poor precision and recall (Optimal F1 score = 0.13). MAIN RESULTS: The reintubation model displayed strong discriminative ability and low predictive error within each validation cohort. CONCLUSIONS: Future work is needed to explore how to optimize models before local implementation.

Tolerability and Acceptability of Autonomous Immersive Virtual Reality Incorporating Digital Twin Technology for Mass Training in Healthcare.

INTRODUCTION: As part of onboarding and systems testing for a clinical expansion, immersive virtual reality (VR) incorporating digital twin technology was used. While digital twin technology has been leveraged by industry, its use in health care has been limited with no prior application for onboarding or training. The tolerability and acceptability of immersive VR for use by a large population of healthcare staff were unknown. METHODS: A prospective, observational study of an autonomous immersive VR onboarding experience to a new clinical space was conducted from May to September 2021. Participants were healthcare staff from several critical care and acute care units. Primary outcomes were tolerance and acceptability measured by reported adverse effects and degree of immersion. Secondary outcomes were attitudes toward the efficacy of VR compared with standard onboarding experiences. RESULTS: A total of 1522 healthcare staff participated. Rates of adverse effects were low and those with prior VR experience were more likely to report no adverse effects. Odds of reporting immersion were high across all demographic groups, though decreased with increasing age. The preference for VR over low-fidelity methods was high across all demographics; however, preferences were mixed when compared with traditional simulation and real-time clinical care. CONCLUSIONS: Large-scale VR onboarding is feasible, tolerable, and acceptable to a diverse population of healthcare staff when using digital twin technology. This study also represents the largest VR onboarding experience to date and may address preconceived notions that VR-based training in health care is not ready for widespread adoption.

Development and User Testing of a Dynamic Tool for Rheumatic Heart Disease Management.

OBJECTIVE: Most rheumatic heart disease (RHD) registries are static and centralized, collecting epidemiological and clinical data without providing tools to improve care. We developed a dynamic cloud-based RHD case management application with the goal of improving care for patients with RHD in Uganda. METHODS: The Active Community Case Management Tool (ACT) was designed to improve community-based case management for chronic disease, with RHD as the first test case. Global and local partner consultation informed selection of critical data fields and prioritization of application functionality. Multiple stages of review and revision culminated in user testing of the application at the Uganda Heart Institute. RESULTS: Global and local partners provided feedback of the application via survey and interview. The application was well received, and top considerations included avenues to import existing patient data, considering a minimum data entry form, and performing a situation assessment to tailor ACT to the health system setup for each new country. Test users completed a postuse survey. Responses were favorable regarding ease of use, desire to use the application in regular practice, and ability of the application to improve RHD care in Uganda. Concerns included appropriate technical skills and supports and potential disruption of workflow. CONCLUSION: Creating the ACT application was a dynamic process, incorporating iterative feedback from local and global partners. Results of the user testing will help refine and optimize the application. The ACT application showed potential for utility and integration into existing care models in Uganda.

From 65 to 103, Older Adults Experience Virtual Reality Differently Depending on Their Age: Evidence from a Large-Scale Field Study in Nursing Homes and Assisted Living Facilities.

There is growing interest in applications of virtual reality (VR) to improve the lives of older adults, but the limited research on older adults and VR largely treats older adults as a monolith, ignoring the substantial differences across 65 to 100+ year olds that may affect their experience of VR. There are also few existing studies examining the experiences and challenges facing those who facilitate VR for older adults (e.g., caregiving staff). We address these limitations through two studies. In study 1, we explore variation within older adults' experiences with VR through a field study of VR use among a large (N = 245) and age-diverse (Mage = 83.6 years, SDage = 7.9, range = 65-103 years) sample of nursing home and assisted living facility residents across 10 U.S. states. Age was negatively associated with the extent to which older adults enjoyed VR experiences. However, the negative relationship between age and older adults' attitudes toward VR was significantly less negative than the relationship between age and their attitudes toward other technologies (cell phones and voice assistants). In study 2, we surveyed caregiving staff (N = 39) who facilitated the VR experiences for older adult residents and found that the caregiving staff generally enjoyed the activity relative to other activities and felt it to be beneficial to their relationship with residents.